gRecs: A Group Recommendation System Based on User Clustering

Ntoutsi, Irene; Stefanidis, Kostas; Nørvåg, Kjetil; Kriegel, Hans‐Peter

doi:10.1007/978-3-642-29035-0_25

Cited by 26 publications

(17 citation statements)

References 4 publications

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“…In our current work, we are developing a Java prototype to add a capability for producing contextual group recommendations to our group recommendations system [17]. There are many directions for future work.…”

Section: Discussionmentioning

confidence: 99%

“…Existing methods to construct a ranked list of recommendations for a group of users can be classified into two approaches [12]. The first approach aggregates the recommendations of each user into a single recommendation list (e.g., [2,4,17]), while the second one creates a joint profile for all users in the group and provides the group with recommendations computed with respect to this joint profile (e.g., [14,25]). In our work, we adopt the second approach to offer context-aware recommendations to groups.…”

Section: Contextual Recommendations For Groupsmentioning

confidence: 99%

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Contextual Recommendations for Groups

Stefanidis

Shabib

Nørvåg

et al. 2012

Lecture Notes in Computer Science

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Abstract. Recommendation systems have received significant attention, with most of the proposed methods focusing on recommendations for single users. Recently, there are also approaches aiming at either group or context-aware recommendations. In this paper, we address the problem of contextual recommendations for groups. We exploit a hierarchical context model to extend a typical recommendation model to a general context-aware one that tackles the information needs of a group. We base the computation of contextual group recommendations on a subset of preferences of the users that present the most similar behavior to the group, that is, the users with the most similar preferences to the preferences of the group members, for a specific context. This subset of preferences includes the ones with context equal to or more general than the given context.

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Section: Discussionmentioning

confidence: 99%

Section: Contextual Recommendations For Groupsmentioning

confidence: 99%

Contextual Recommendations for Groups

Stefanidis

Shabib

Nørvåg

et al. 2012

Lecture Notes in Computer Science

Self Cite

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“…An important aspect to notice is that no approach in the literature considers a scenario in which the number of recommendation lists is limited. Even if in [39,40] clustering is used in a group recommendation approach, similarly to what we have done in previous works [6][7][8], clusters are used only to select the number of neighbors of a user when predictions are built, in order to speed up the computation. This is the first time that the group recommendation with automatic group detection is explored in its entirety, and the next sections will present the work on each component of a system.…”

Section: Motivation and Contributionmentioning

confidence: 98%

ART: group recommendation approaches for automatically detected groups

Boratto

Carta

2015

Int. J. Mach. Learn. & Cyber.

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Group recommender systems provide suggestions when more than a person is involved in the recommendation process. A particular context in which group recommendation is useful is when the number of recommendation lists that can be generated is limited (i.e., it is not possible to suggest a list of items to each user). In such a case, grouping users and producing recommendations to groups becomes necessary. None of the approaches in the literature is able to automatically group the users in order to overcome the previously presented limitation. This paper presents a set of group recommender systems that automatically detect groups of users by clustering them, in order to respect a constraint on the maximum number of recommendation lists that can be produced. The proposed systems have been largely evaluated on two real-world datasets and compared with hundreds of experiments and statistical tests, in order to validate the results. Moreover, we introduce a set of best practices that help in the development of group recommender systems in this context.

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“…Ntoutsi, Stefanidis et al 2012 [20] apply user clustering algorithm for organizing users into clusters of users with similar preferences and then exploit a top-k algorithm to efficiently recommend k items to the group. This paper assumes latent factor interests and provides a full matrix to detect groups.…”

Section: Moviepilot Is Released As Part Of Context-aware Moviementioning

confidence: 99%

A Latent Group Model for Group Recommendation

Shi

Lin

2015

2015 IEEE International Conference on Mobile Services

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Increasingly, thousands of mobile services are provided by mobile Internet service portals. In order to push information that users may fond of, recommender system is needed. In some circumstances, recommendation to groups is necessary, e.g., recommending movies to a group of friends. In reality, users are in some hidden social network, which can be viewed as groups. So group recommendation is proposed. Time efficiency is a key problem in mobile group recommendation. Research on group recommendation have concentrated on two approaches: aggregating members' ratings into a group profile and aggregating users' recommendations into a group recommendation list. This paper proposes a latent group modelLGM, based on the assumption that users are influenced implicitly by some latent factors. LGM presents a novel route to detect groups by taking latent factors into account and makes users' profiles exist in latent factor format. Then users' latent factor profiles are aggregated into a group profile and multiplying method is used for group recommendation. This paper compares LGM with two approaches proposed before in efficiency and accuracy. It achieves better efficiency and accuracy for group recommendation on MovieLens dataset.

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gRecs: A Group Recommendation System Based on User Clustering

Cited by 26 publications

References 4 publications

Contextual Recommendations for Groups

Contextual Recommendations for Groups

ART: group recommendation approaches for automatically detected groups

A Latent Group Model for Group Recommendation

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